{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from scipy import sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DATA_PATH = \"./ppd_data/\"\n",
    "TRAIN_PATH = DATA_PATH + \"train.csv\"\n",
    "TEST_PATH = DATA_PATH + \"test.csv\"\n",
    "WORD_EMBED_PATH = DATA_PATH + \"word_embed.txt\"\n",
    "CHAR_EMBED_PATH = DATA_PATH + \"cahr_embed.txt\"\n",
    "QUEST_PATH = DATA_PATH + \"question.csv\"\n",
    "\n",
    "SEED = 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_raw = pd.read_csv(TRAIN_PATH)\n",
    "test_raw = pd.read_csv(TEST_PATH)\n",
    "question = pd.read_csv(QUEST_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "question[\"words\"] = question[\"words\"].str.split(\" \")\n",
    "question[\"chars\"] = question[\"chars\"].str.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qid</th>\n",
       "      <th>words</th>\n",
       "      <th>chars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q397345</td>\n",
       "      <td>[W04465, W04058, W05284, W02916]</td>\n",
       "      <td>[L2218, L2568, L0360, L0242, L2218, L0741]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q193805</td>\n",
       "      <td>[W10054, W04476, W09996, W12244, W18103]</td>\n",
       "      <td>[L2376, L2168, L0050, L1187, L0104, L2432, L09...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q085471</td>\n",
       "      <td>[W04346, W17378, W19355, W17926, W14185, W1156...</td>\n",
       "      <td>[L2323, L1526, L2214, L1132, L2723, L1861, L22...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q189314</td>\n",
       "      <td>[W17508, W09996, W19662, W17534, W11399, W1705...</td>\n",
       "      <td>[L0018, L2321, L1346, L2432, L0902, L1149, L19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q267714</td>\n",
       "      <td>[W13157, W03390, W01952, W05789, W17378, W0871...</td>\n",
       "      <td>[L2271, L1346, L1389, L2932, L0466, L2218, L19...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       qid                                              words  \\\n",
       "0  Q397345                   [W04465, W04058, W05284, W02916]   \n",
       "1  Q193805           [W10054, W04476, W09996, W12244, W18103]   \n",
       "2  Q085471  [W04346, W17378, W19355, W17926, W14185, W1156...   \n",
       "3  Q189314  [W17508, W09996, W19662, W17534, W11399, W1705...   \n",
       "4  Q267714  [W13157, W03390, W01952, W05789, W17378, W0871...   \n",
       "\n",
       "                                               chars  \n",
       "0         [L2218, L2568, L0360, L0242, L2218, L0741]  \n",
       "1  [L2376, L2168, L0050, L1187, L0104, L2432, L09...  \n",
       "2  [L2323, L1526, L2214, L1132, L2723, L1861, L22...  \n",
       "3  [L0018, L2321, L1346, L2432, L0902, L1149, L19...  \n",
       "4  [L2271, L1346, L1389, L2932, L0466, L2218, L19...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_total = pd.DataFrame(pd.concat([train_raw[\"q1\"], train_raw[\"q2\"]]), columns=[\"qid\"])\n",
    "train_total = train_total.merge(question, how=\"left\", on=\"qid\")\n",
    "train_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_words = train_total[\"words\"].tolist()\n",
    "train_chars = train_total[\"chars\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import TfidfModel\n",
    "\n",
    "word_dict = Dictionary(train_words)\n",
    "char_dict = Dictionary(train_chars)\n",
    "word_corpus = [word_dict.doc2bow(stc) for stc in train_words]\n",
    "char_corpus = [char_dict.doc2bow(stc) for stc in train_chars]\n",
    "\n",
    "word_tfidf_model = TfidfModel(word_corpus)\n",
    "char_tfidf_model = TfidfModel(char_corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_word_q1, train_word_q2 = train_words[:len(train_raw)], train_words[len(train_raw):]\n",
    "train_char_q1, train_char_q2 = train_chars[:len(train_raw)], train_chars[len(train_raw):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word_corpus1 = [word_dict.doc2bow(stc) for stc in train_word_q1]\n",
    "word_corpus2 = [word_dict.doc2bow(stc) for stc in train_word_q2]\n",
    "char_corpus1 = [char_dict.doc2bow(stc) for stc in train_char_q1]\n",
    "char_corpus2 = [char_dict.doc2bow(stc) for stc in train_char_q2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word_tfidf1 = [word_tfidf_model[stc] for stc in word_corpus1]\n",
    "word_tfidf2 = [word_tfidf_model[stc] for stc in word_corpus2]\n",
    "char_tfidf1 = [char_tfidf_model[stc] for stc in char_corpus1]\n",
    "char_tfidf2 = [char_tfidf_model[stc] for stc in char_corpus2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def vec2sparse(vec, row_num, col_num):\n",
    "    rows = [i for i, stc in enumerate(vec) for word in stc]\n",
    "    cols = [word[0] for i, stc in enumerate(vec) for word in stc]\n",
    "    data = [word[1] for i, stc in enumerate(vec) for word in stc]\n",
    "    return sparse.csr_matrix((data, (rows, cols)), shape=(row_num, col_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "word_sparse1 = vec2sparse(word_tfidf1, len(train_raw), len(word_dict.dfs))\n",
    "word_sparse2 = vec2sparse(word_tfidf2, len(train_raw), len(word_dict.dfs))\n",
    "char_sparse1 = vec2sparse(char_tfidf1, len(train_raw), len(char_dict.dfs))\n",
    "char_sparse2 = vec2sparse(char_tfidf2, len(train_raw), len(char_dict.dfs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "w1_train, w1_test, w2_train, w2_test, c1_train, c1_test, c2_train, c2_test, y_train, y_test = \\\n",
    "    train_test_split(word_sparse1, word_sparse2, char_sparse1, char_sparse2, train_raw[\"label\"],\n",
    "                     test_size=0.2, random_state=SEED, stratify=train_raw[\"label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate features by stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# X_stack_train = sparse.hstack([w1_train, c1_train, w2_train, c2_train])\n",
    "# X_stack_test = sparse.hstack([w1_test, c1_test, w2_test, c2_test])\n",
    "# X_stack_train, X_stack_test\n",
    "\n",
    "# train_data = xgb.DMatrix(X_stack_train, label=y_train)\n",
    "# dev_data = xgb.DMatrix(X_stack_test, label=y_test)\n",
    "# train_data, dev_data\n",
    "\n",
    "# params = {}\n",
    "# params[\"objective\"] = \"binary:logistic\"\n",
    "# params[\"eval_metric\"] = \"logloss\"\n",
    "# params[\"seed\"] = SEED\n",
    "# params[\"eta\"] = 0.01\n",
    "# params[\"max_depth\"] = 6\n",
    "\n",
    "# watch_list = [(train_data, \"train\"), (dev_data, \"dev\")]\n",
    "# boost = xgb.train(params, train_data, num_boost_round=50000, evals=watch_list, early_stopping_rounds=100, verbose_eval=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generate features by sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<203508x13498 sparse matrix of type '<class 'numpy.float64'>'\n",
       " \twith 5792961 stored elements in COOrdinate format>,\n",
       " <50878x13498 sparse matrix of type '<class 'numpy.float64'>'\n",
       " \twith 1448513 stored elements in COOrdinate format>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sub_train = sparse.hstack([w1_train - w2_train, c1_train - c2_train])\n",
    "X_sub_test = sparse.hstack([w1_test - w2_test, c1_test - c2_test])\n",
    "X_sub_train, X_sub_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-logloss:0.690786\tdev-logloss:0.690802\n",
      "Multiple eval metrics have been passed: 'dev-logloss' will be used for early stopping.\n",
      "\n",
      "Will train until dev-logloss hasn't improved in 100 rounds.\n",
      "[10]\ttrain-logloss:0.671249\tdev-logloss:0.671509\n",
      "[20]\ttrain-logloss:0.655239\tdev-logloss:0.655689\n",
      "[30]\ttrain-logloss:0.64251\tdev-logloss:0.643119\n",
      "[40]\ttrain-logloss:0.631766\tdev-logloss:0.632544\n",
      "[50]\ttrain-logloss:0.621635\tdev-logloss:0.622587\n",
      "[60]\ttrain-logloss:0.612459\tdev-logloss:0.61358\n",
      "[70]\ttrain-logloss:0.604683\tdev-logloss:0.605881\n",
      "[80]\ttrain-logloss:0.597493\tdev-logloss:0.598814\n",
      "[90]\ttrain-logloss:0.590834\tdev-logloss:0.59233\n",
      "[100]\ttrain-logloss:0.58491\tdev-logloss:0.586486\n",
      "[110]\ttrain-logloss:0.579317\tdev-logloss:0.580983\n",
      "[120]\ttrain-logloss:0.574262\tdev-logloss:0.576003\n",
      "[130]\ttrain-logloss:0.569337\tdev-logloss:0.571155\n",
      "[140]\ttrain-logloss:0.564862\tdev-logloss:0.566803\n",
      "[150]\ttrain-logloss:0.560625\tdev-logloss:0.56263\n",
      "[160]\ttrain-logloss:0.5567\tdev-logloss:0.558866\n",
      "[170]\ttrain-logloss:0.552907\tdev-logloss:0.555163\n",
      "[180]\ttrain-logloss:0.549598\tdev-logloss:0.551969\n",
      "[190]\ttrain-logloss:0.546221\tdev-logloss:0.548741\n",
      "[200]\ttrain-logloss:0.54304\tdev-logloss:0.545686\n",
      "[210]\ttrain-logloss:0.540053\tdev-logloss:0.542756\n",
      "[220]\ttrain-logloss:0.537117\tdev-logloss:0.539925\n",
      "[230]\ttrain-logloss:0.534249\tdev-logloss:0.537164\n",
      "[240]\ttrain-logloss:0.531582\tdev-logloss:0.534581\n",
      "[250]\ttrain-logloss:0.529135\tdev-logloss:0.532243\n",
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      "[270]\ttrain-logloss:0.523919\tdev-logloss:0.52728\n",
      "[280]\ttrain-logloss:0.521542\tdev-logloss:0.525023\n",
      "[290]\ttrain-logloss:0.51901\tdev-logloss:0.522626\n",
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      "[320]\ttrain-logloss:0.512794\tdev-logloss:0.516736\n",
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      "[360]\ttrain-logloss:0.505258\tdev-logloss:0.50964\n",
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      "[400]\ttrain-logloss:0.497914\tdev-logloss:0.502738\n",
      "[410]\ttrain-logloss:0.496259\tdev-logloss:0.501222\n",
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      "[1080]\ttrain-logloss:0.429876\tdev-logloss:0.441156\n",
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      "[58980]\ttrain-logloss:0.132632\tdev-logloss:0.292712\n",
      "[58990]\ttrain-logloss:0.132605\tdev-logloss:0.292716\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[59000]\ttrain-logloss:0.132576\tdev-logloss:0.292722\n",
      "[59010]\ttrain-logloss:0.132556\tdev-logloss:0.292723\n",
      "[59020]\ttrain-logloss:0.132545\tdev-logloss:0.292724\n",
      "[59030]\ttrain-logloss:0.132502\tdev-logloss:0.292716\n",
      "[59040]\ttrain-logloss:0.13248\tdev-logloss:0.292708\n",
      "[59050]\ttrain-logloss:0.132451\tdev-logloss:0.292699\n",
      "[59060]\ttrain-logloss:0.132431\tdev-logloss:0.2927\n",
      "[59070]\ttrain-logloss:0.132419\tdev-logloss:0.292701\n",
      "[59080]\ttrain-logloss:0.132403\tdev-logloss:0.292699\n",
      "[59090]\ttrain-logloss:0.132382\tdev-logloss:0.292697\n",
      "[59100]\ttrain-logloss:0.132355\tdev-logloss:0.292688\n",
      "[59110]\ttrain-logloss:0.132332\tdev-logloss:0.292684\n",
      "[59120]\ttrain-logloss:0.132306\tdev-logloss:0.292679\n",
      "[59130]\ttrain-logloss:0.132289\tdev-logloss:0.292678\n",
      "[59140]\ttrain-logloss:0.132269\tdev-logloss:0.292675\n",
      "[59150]\ttrain-logloss:0.132249\tdev-logloss:0.292674\n",
      "[59160]\ttrain-logloss:0.132236\tdev-logloss:0.292673\n",
      "[59170]\ttrain-logloss:0.132225\tdev-logloss:0.292669\n",
      "[59180]\ttrain-logloss:0.13221\tdev-logloss:0.292671\n",
      "[59190]\ttrain-logloss:0.132178\tdev-logloss:0.292669\n",
      "[59200]\ttrain-logloss:0.132162\tdev-logloss:0.292664\n",
      "[59210]\ttrain-logloss:0.132139\tdev-logloss:0.292659\n",
      "[59220]\ttrain-logloss:0.132113\tdev-logloss:0.292654\n",
      "[59230]\ttrain-logloss:0.132085\tdev-logloss:0.292651\n",
      "[59240]\ttrain-logloss:0.132067\tdev-logloss:0.292648\n",
      "[59250]\ttrain-logloss:0.132048\tdev-logloss:0.292646\n",
      "[59260]\ttrain-logloss:0.132034\tdev-logloss:0.292646\n",
      "[59270]\ttrain-logloss:0.132001\tdev-logloss:0.292639\n",
      "[59280]\ttrain-logloss:0.131982\tdev-logloss:0.292638\n",
      "[59290]\ttrain-logloss:0.131964\tdev-logloss:0.292635\n",
      "[59300]\ttrain-logloss:0.131928\tdev-logloss:0.292629\n",
      "[59310]\ttrain-logloss:0.131912\tdev-logloss:0.292631\n",
      "[59320]\ttrain-logloss:0.131896\tdev-logloss:0.292638\n",
      "[59330]\ttrain-logloss:0.131884\tdev-logloss:0.292641\n",
      "[59340]\ttrain-logloss:0.131874\tdev-logloss:0.29264\n",
      "[59350]\ttrain-logloss:0.131839\tdev-logloss:0.292634\n",
      "[59360]\ttrain-logloss:0.131816\tdev-logloss:0.292631\n",
      "[59370]\ttrain-logloss:0.131804\tdev-logloss:0.292634\n",
      "[59380]\ttrain-logloss:0.131788\tdev-logloss:0.292636\n",
      "[59390]\ttrain-logloss:0.131775\tdev-logloss:0.292634\n",
      "Stopping. Best iteration:\n",
      "[59297]\ttrain-logloss:0.131937\tdev-logloss:0.292629\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sub_train_data = xgb.DMatrix(X_sub_train, label=y_train)\n",
    "sub_dev_data = xgb.DMatrix(X_sub_test, label=y_test)\n",
    "\n",
    "sub_params = {}\n",
    "sub_params[\"objective\"] = \"binary:logistic\"\n",
    "sub_params[\"eval_metric\"] = \"logloss\"\n",
    "sub_params[\"seed\"] = SEED\n",
    "sub_params[\"eta\"] = 0.02\n",
    "sub_params[\"max_depth\"] = 5\n",
    "\n",
    "sub_watch_list = [(sub_train_data, \"train\"), (sub_dev_data, \"dev\")]\n",
    "sub_boost = xgb.train(sub_params, sub_train_data, num_boost_round=100000, evals=sub_watch_list,\n",
    "                      early_stopping_rounds=100, verbose_eval=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_q1 = test_raw.merge(question, how=\"left\", left_on=\"q1\", right_on=\"qid\").drop([\"q1\", \"q2\", \"qid\"], axis=1)\n",
    "test_q2 = test_raw.merge(question, how=\"left\", left_on=\"q2\", right_on=\"qid\").drop([\"q1\", \"q2\", \"qid\"], axis=1)\n",
    "\n",
    "test_word1 = [word_tfidf_model[word_dict.doc2bow(stc)] for stc in test_q1[\"words\"].tolist()]\n",
    "test_word2 = [word_tfidf_model[word_dict.doc2bow(stc)] for stc in test_q2[\"words\"].tolist()]\n",
    "test_char1 = [char_tfidf_model[char_dict.doc2bow(stc)] for stc in test_q1[\"chars\"].tolist()]\n",
    "test_char2 = [char_tfidf_model[char_dict.doc2bow(stc)] for stc in test_q2[\"chars\"].tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_word_sparse1 = vec2sparse(test_word1, len(test_raw), len(word_dict.dfs))\n",
    "test_word_sparse2 = vec2sparse(test_word2, len(test_raw), len(word_dict.dfs))\n",
    "test_char_sparse1 = vec2sparse(test_char1, len(test_raw), len(char_dict.dfs))\n",
    "test_char_sparse2 = vec2sparse(test_char2, len(test_raw), len(char_dict.dfs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xgboost.core.DMatrix at 0x7fd82e4d4fd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = sparse.hstack([test_word_sparse1 - test_word_sparse2, test_char_sparse1 - test_char_sparse2])\n",
    "X_test_data = xgb.DMatrix(X_test)\n",
    "X_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21560663, 0.9803411 , 0.09555773, ..., 0.919225  , 0.134799  ,\n",
       "       0.0748089 ], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = sub_boost.predict(X_test_data)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.215607\n",
       "1    0.980341\n",
       "2    0.095558\n",
       "3    0.937629\n",
       "4    0.542689\n",
       "Name: y_pre, dtype: float32"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_res = pd.Series(pred, name=\"y_pre\")\n",
    "pred_res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_res.to_csv(\"./pred.csv\", index=False, header=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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